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This study adopted femtosecond laser with a wavelength of 515 nm to drill high-aspect-ratio micro through holes on a 500-μm thickness single-crystal SI-type 4H-SiC wafer. Firstly, through holes with a high aspect ratio of 20 were fabricated in air. However, the heat affect zone (HAZ), cracks, and surface material shedding around entrances and exits of the holes are inevitable in air even after chemical corrosion post-processing. In order to remove these defects, the water-assisted femtosecond laser drilling of 4H-SiC was investigated. The high-quality through holes free of cracks, surface material shedding, and HAZ were obtained under the action of internal scour and heat diffusion of water. Besides, the water layer thickness and the laser repetition frequency have a great influence on the processing quality and efficiency of the micro-holes. Finally, high-quality high-ratio-rate through micro-hole arrays on 4H-SiC were fabricated with the optimal process parameters, which is significant for the development of SiC electronic devices and the high-quality micro-fabrication of other hard and brittle materials.

Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber-physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing.

This study is mainly to provide an overview of human walking induced energy harvest. Focusing on the proportion of all energy sources provided by daily activity, the available human walking induced energy is divided with respect to the generation principle. The extensive research on harvesting energy results from body vibration, inertial element, and foot press to convert into electricity is overviewed. Over the past decades, various smart materials have been employed to achieve energy conversion. Generators based on electromagnetic induction or the triboelectric effect were developed and integrated. Small captured power and low overall efficiency are criticized. The concept of human walking energy harvest is extended into the wearable walking robotics using other mediums, such as fluid, to transmit power instead of electricity. By comparison, it is indicated that less energy conversion links are involved in energy regeneration of such applications and expected to guarantee less loss and higher efficiency. Meanwhile, in order to overcome the shortage of relatively low power output, comments are made that the harvester should be capable of adaptation under the condition that the mechanical energy of lower limb and feet is subject to change in different gait phases so as to maximize the collected energy.

The permanent magnet synchronous motor (PMSM) servo system is widely applied in many industrial fields due to its unique advantages. In this paper, we study the deep reinforcement learning (DRL) speed control strategy for PMSM servo system, in which exist many disturbances, i.e., load torque and rotational inertia variations. The speed control problem is formulated as a Markov decision process problem, which is computed optimal regulation scheme corresponding to each speed and error state using the deep Q-networks. Simulation results are provided to demonstrate that compared with conventional proportion integral control, the proposed DRL control can improve the robustness against load disturbances and high performance of the PMSM speed control system.

It is well known that thermal error has a significant impact on the accuracy of CNC machine tools. In order to decrease the thermally induced positioning error of machine tools, a novel thermal error modeling approach based on Bayesian neural network is proposed in this paper. The relationship between the temperature rise and positioning error of the feed drive system is investigated by simultaneously measuring the thermal characteristics that include the temperature field and positioning error of the CNC machine tool. Fuzzy c-means (FCM) clustering and correlation analysis are used to select temperature-sensitive points, and the Dunn index is introduced to determine the optimal number of clustering groups, which can inhibit the multicollinearity problem among temperature measuring points effectively. The least-square linear fitting is applied to explore the feature of the positioning error data. The results show that compared with the BP neural network and multiple linear regression model, the Bayesian neural network not only has higher prediction accuracy but also can guarantee excellent prediction performance under different working conditions. The prediction results obtained under different operating conditions indicate that the maximum thermal error can be reduced from around 18.2 to 5.14 μm by using the Bayesian neural network, which represents a 71% reduction in the thermally induced error of the feed drive system of machine tool.

The characteristics of geometric error affect both the positions and orientations of a five-axis machine tool, which are very important for precision manufacturing. It is necessary to conduct quantitative analysis for the above characteristics to improve the precision of the five-axis machine tool. In this paper, the synthetic volumetric error model of the five-axis machine tool with a turntable-tilting head has been established, which describes the effect of 43 geometric error terms on position and orientation error vector intuitively. The multidimensional output of geometric error vectors in the workspace of the machine tool is sufficiently taken into account, and global quantitative sensitivity analysis is introduced to determine the effect of each geometric error on the precision of the machine tool. The results showed that geometric errors of the rotary axes are dominant sensitivity factors, reaching 59.32 and 51.59% of sensitivity indices of the position and orientation error vector, respectively. Furthermore, geometric error terms that are noncritical and critical are extracted according to the result of mutual information analysis. Those geometric errors were removed from the geometric error compensation model, which are at the same time insensitivity errors and nonsignificant geometric errors. The geometric error compensation results show that the accuracy of the machined parts with complex curved surfaces was improved 56.22% after error compensation based on sensitivity and mutual information analysis. This research provides a feasible methodology for analyzing the effect of geometric errors and determining the compensation values of the machine tool.

The five-axis tool path generated by CAM software usually consists of a series of linear paths. The tangent direction at the corner of the adjacent line segment will suddenly change, and the curvature is also discontinuous, which will cause vibration and shock during the machining process. Thus, a global corner smoothing algorithm based on cubic NURBS interpolation is proposed to smooth the linear paths in this paper, so as to achieve G2 continuous for five-axis linear paths. The algorithm proposed does not require matrix operations to solve the control points, and it can also reduce the number of control points while satisfying the interpolation error. The algorithm is then used to generate smooth NURBS path for ceramic core burrs and blockage repair. The simulation and experiment show that the algorithm proposed can satisfy the error constraints, reduce the vibration of the motion axis, and improve the surface quality of laser cutting.

Understanding the complex interactions between climate change and stand developmental dynamics in forest growth and carbon sequestration is essential for implementing sustainable management under climate change and for supporting China’s dual-carbon goals. Using data from 243 permanent national forest inventory plots (each 0.0667 ha in size), this study developed a climate- and stage-sensitive forest growth and yield model (FGYM) for natural Larix gmelinii forests in Northeast China. The model incorporates the De Martonne aridity index (MAI) to represent climatic water availability and the stand developmental stage index, categorized into Stage 1 (early), Stage 2 (middle), and Stage 3 (late), to capture the intrinsic biological progression of forest structure. It simultaneously simulates (i) stand basis structure attributes, (ii) timber yields across different assortments, and (iii) carbon stocks in different tree components and end-use categories. Comparative analyses demonstrated that the stage-sensitive model outperformed the baseline models, revealing pronounced stage- and climate-dependent divergences in stand volume and carbon stock trajectories. For a representative stand [age = 100 years, site class index (SCI) = 16 m], the stage-sensitive model predicted 2.96% higher volume and 3.11% higher carbon stocks at Stage 2, but 15.02% and 15.70% lower values at Stage 3, indicating strong sensitivity to ontogenetic transitions and increasing climatic aridity. Across all combinations of MAI, SCI, and developmental stage, the FGYM consistently captured structural and carbon dynamics that the conventional model did not reproduce. Our findings highlight that integrating both climatic drivers and developmental heterogeneity substantially enhances model accuracy and ecological realism, providing a robust tool for assessing the future productivity and carbon sequestration potential of L. gmelinii forests under future climate change scenarios.

In order to avoid high-temperature and large rate discharge impact on the performance of battery modules, a microchannel liquid cooling battery thermal management system (BTMS) and BTMS virtual model of the microchannel structure based on digital twin (DT) is proposed. On the basis of accurate virtual simulation model, the computational fluid dynamics (CFD) model and the Gaussian process regression algorithm were combined to drive the optimization process in order to improve the cooling capacity of the system. The results show that the microchannel plates can greatly enhance the cooling capacity of the direct cooling system and effectively improve the uniformity of the coolant. The width of the microchannel plates and the side spacing actually represent the amount of coolant flowing through the inside and outside of the battery module, which significantly impacts the maximum temperature and maximum temperature difference. Increasing the coolant flow can only effectively improve the cooling capacity of the module to a limited extent. Gaussian process regression based on the DT virtual model is more suitable for analyzing the interaction between multiple factors and obtaining global optimization results. After optimization, the maximum temperature and the maximum temperature difference of the system are reduced by 4.02 °C and 5.05 °C, respectively. The proposed structure and method are expected to provide insights into the design and development of battery thermal management systems.

To improve the accuracy of five-axis machine tool with a swiveling head, an approach for optimizing compensation values taking into account the sensitivity of position-independent geometric error is proposed in this paper. At first, the synthetic volumetric error model of five-axis machine tool has been established with homogeneous transformation method and multibody system theory, which describes the effect of geometric error components on position and orientation error vector intuitively. Second, the probability distributions of geometric errors in workspace of machine tool are sufficiently taken into account in order to overcome the defects of the analysis at certain locations, and then, global quantitative sensitivity analysis is introduced for determining the effect of each geometric error on precision of machine tool. Next, the optimum values are obtained by multiobjective quality loss and precision robustness trade-offs based on archive-based microgenetic algorithm (AMGA) for geometric error compensation. Finally, the geometric error compensation experiments were carried out, and the results show that the accuracy of measuring trajectories are improved significantly, which reaches 73.7% after error compensation with optimum values based on sensitivity analysis. Hence, the proposed methodology of analysis and compensation are effective for analyzing the effect of geometric errors and improving the precision of machine tool.